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HiFi-123: Towards High-fidelity One Image to 3D Content Generation
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Recent advances in diffusion models have enabled 3D generation from a single image. However, current methods often produce suboptimal results for novel views, with blurred textures and deviations from the reference image, limiting their practical applications. In this paper, we introduce HiFi-123, a method designed for high-fidelity and multi-view consistent 3D generation. Our contributions are twofold: First, we propose a Reference-Guided Novel View Enhancement (RGNV) technique that significantly improves the fidelity of diffusion-based zero-shot novel view synthesis methods. Second, capitalizing on the RGNV, we present a novel Reference-Guided State Distillation (RGSD) loss. When incorporated into the optimization-based image-to-3D pipeline, our method significantly improves 3D generation quality, achieving state-of-the-art performance. Comprehensive evaluations demonstrate the effectiveness of our approach over existing methods, both qualitatively and quantitatively. Video results are available on the project page.
Forward citations
Cited by 4 Pith papers
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PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models
A multi-view diffusion pipeline that segments 3D objects into parts, completes occluded or invisible parts, and reconstructs them into a compositional 3D asset.
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A two-stage cascaded video diffusion model generates 16-view consistent videos from a monocular video, enabling higher-quality 4D content reconstruction.
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AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scene
AE-NeRF jointly optimizes camera poses and an event-based NeRF with a proposal network and four event-specific losses, improving novel view synthesis under noisy poses and non-uniform motion.
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RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing
A two-stage, zero-shot diffusion pipeline that textures room-scale meshes with global style consistency and per-instance repainting.
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